Unsupervised Topical Organization of Documents using Corpus-based Text Analysis

Abstract

This study aims at automating the process of topical keyword organization of set of documents in an input text corpus. It is conducted in the context of a larger project to investigate efficient unsupervised learning techniques to automatically extract relevant classes and their keyword descriptions from a set of the United Nations (UN) documents, and use the latter to produce reference corpora allowing to classify future UN documents. We assume that the reference classes are unknown in advance, and thus suggest an unsupervised clustering approach which accepts as input a bunch of unstructured text documents, and produces as output groups of similar documents describing similar topics. The input document feature vectors are augmented with term co-occurrence and relatedness scores produced from a distributional thesaurus built on the same (or a related) corpus. The augmented feature vectors are then run through a hierarchical clustering process to identify groups of similar documents, which serve as candidates for topical organization and keyword extraction. Experiments on a manually labelled dataset of documents classified against the UN's Sustainable Development Goals (SDGs) confirm the quality and potential of the approach.